https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE119290

if (!requireNamespace("knitr", quietly = TRUE))
    install.packages("knitr")
if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
if (!("DESeq2" %in% installed.packages())) {
  BiocManager::install("DESeq2", update = FALSE)
}
if (!("EnhancedVolcano" %in% installed.packages())) {
  BiocManager::install("EnhancedVolcano", update = FALSE)
}
if (!("apeglm" %in% installed.packages())) {
  BiocManager::install("apeglm", update = FALSE)
}
if (!("pheatmap" %in% installed.packages())) {
  BiocManager::install("pheatmap", update = FALSE)
}
if (!("gprofiler2" %in% installed.packages())) {
  BiocManager::install("gprofiler2", update = FALSE)
}
if (!("clusterProfiler" %in% installed.packages())) {
  BiocManager::install("clusterProfiler", update = FALSE)
}
if (!("M3C" %in% installed.packages())) {
  BiocManager::install("M3C", update = FALSE)
}
library(M3C)
library(umap)

Attaching package: ‘umap’

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library(Rtsne)
library(clusterProfiler)

Registered S3 method overwritten by 'data.table':
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clusterProfiler v4.0.5  For help: https://yulab-smu.top/biomedical-knowledge-mining-book/

If you use clusterProfiler in published research, please cite:
T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141. doi: 10.1016/j.xinn.2021.100141

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library ("pheatmap")
library ("RColorBrewer")
library(magrittr)
library(matrixStats)
library(ggplot2)
library(DESeq2)
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Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with 'browseVignettes()'.
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library(DOSE)
DOSE v3.18.2  For help: https://guangchuangyu.github.io/software/DOSE

If you use DOSE in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis. Bioinformatics 2015, 31(4):608-609
library('org.Hs.eg.db')
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data.table 1.14.0 using 6 threads (see ?getDTthreads).  Latest news: r-datatable.com

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###### Load the data into R.######
matrix_of_data <- as.matrix(read.table("GSE119290_Readhead_2018_RNAseq_gene_counts.txt"))
coldata<-read.csv("coldata.csv",header = T,row.names=1,stringsAsFactors=T)
##### calculate per-gene expression ranges and generating a density plot #####
matrix_of_ranges <- rowRanges(matrix_of_data, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(matrix_of_data), useNames = NA)

vector_of_ranges <- 0
for(i in 1:26364) {
  vector_of_ranges[i] <- matrix_of_ranges[i,2]-matrix_of_ranges[i,1]
}
vector_of_ranges
   [1]     41   1767    132    132    132    132      5      5      5      5      3
  [12]      3      1   1159   3270   1598   1598     22     22     22      6   6795
  [23]    780     39    124   1111     28    102   2021   7450    888     32     15
  [34]   1169   1133  98732      4   1028     41      1      1      1     14     77
  [45]      6  10563   4508     43   3752    167   4599      8   1832   4976     51
  [56]   1677     99   4426     40   1061   7033   5415   1625   5290    129     15
  [67]     11   1244    367   3203   5804    274   5384   1102   3048    307    597
  [78]   4254  10132   2897   7032   3740  49505     12     32     28     10   1705
  [89]   3949  13540    706    209   6716   3721     53   2980   2161     28    117
 [100]     13   2897    634     10      2    207   2181      1    534    969      3
 [111]    923    833    525   3940     10     53   3687   1822    561   1697     25
 [122]      2      0    139      0      3   1687    359    119  17325     23    188
 [133]   8122     21    388   3736   7508    374    378      1   1179   3495   3052
 [144]      7   1133   3149   3152   2788   3824      2   2872   3570    599     14
 [155]     35  13253  21306      2    900  24204     20 157014    124     22      2
 [166]      2     10     50      8   6421    500    231    910   4853   1140      1
 [177]     72  21989   2971   1317    617      4     25   4932  39241     95  19906
 [188]    902    897    210   2739   1447    878     15  14160    275   8176   5336
 [199]   8058    176     35   1376   2751      2    222   1121    268   7510   1895
 [210]   1698    357      2   1425   5122   1131    366    794   2488  10403   7352
 [221]   2488     17     13      1   1447      1   3904     28     28     79      2
 [232]      1      1      7      4      3      2      8      8      8      3      2
 [243]      3      3      2      1      3      1      1      1      1      2      7
 [254]      7      6      2      2      3      5      5      0      1      2      2
 [265]      5  11017   4457   3006    799   1200      5     81   2735      9      3
 [276]     40    532   1530     78    775    641   5069    185      3   6697    211
 [287]    582  12264      9   3009      6      2      5     18     32  21303    539
 [298]    565   2331  11170      9   2800   1331      1      0   7928   2279     69
 [309]      2      2      5     68   3412   2901   3994   2805    183     12      9
 [320]      0      5      3  18070   4617    567      1     26    740   1813      2
 [331]   3029      4      5   4281  17509   8807   8857     95    151    707   2586
 [342]   1738  13335   5025   2535     49   1127      3     84      0   2144      1
 [353]     11      3      6      3      2     31    238     39      8   5195   2223
 [364]     15      3   3255     72   3644  10838   1015   2591  15594   9933      5
 [375]  17752     18    536   1613   1066   4797   2635  16293      1      1     40
 [386]    570  14671   3622      0   3041     17      0      1      1      1  15623
 [397]      1    518      5      6  12259     10      6   4128    129    331  23559
 [408]   8387   1018     63   4543   2741    100  22553      3  29344   4179    935
 [419]   3462   4903   1563   1042    504      4      0   7498  12001      4      6
 [430]      9      4     58    342    815      1     90     26      8   8991  21268
 [441]     16      0      0   1639   1339     59   4163     79   1386    724   5571
 [452]  21117   2896   1106    843   1141  41159   2450    777     97      3      3
 [463]   1144     65   1962    187      2      4   2725   7751   1651      3     86
 [474]      4   4013   1490     41  38661   2325      2  18825    450   2091     64
 [485]   1570    851      3  10816     14   1205   1767   1479   4063   2372    999
 [496]     60   1435     24      6    332   8754   3491      2    916    451   3444
 [507]   3776      9    155   3773     15    255   2095     25   6935   4965   4266
 [518]   1116   6348   4278   8453     61    829    887     45    190     45     58
 [529]     61    727     13   1005   5527     58  12478   1535   8136   1081   4099
 [540]      2      2     17     13     38      0  58965   8804     48     48      8
 [551]   4623   4205   2561    796   4389      1      4     59      8   4827    268
 [562]  27561      9   1472   9841  13416   1426      9   7058   5237   1884    435
 [573]    119    445  11258    212     29      4   6424  58752    133    214   2859
 [584]    406   1225   1072  20138   4261   1299   7011   2394   1175    416   3532
 [595]   1964   3419    223    890   4260     18  12015    657     21    336    795
 [606]      2      3     40      0     22      8     42     19   2642    356     76
 [617]    375    863   1679  34128   7909   5890   4032     92  13020   1311   6221
 [628]   2357  10820   1650    531   1944     27   2058  11474  12352    206   3390
 [639]   9894   2738    775   2917      3   1165      0     60    169      4   2908
 [650]     28   1154   3216   3662     42   1609   5663     42   3752   1483   5248
 [661]   1703   1228   5631   1294   2270   1357      6     17   1455   1736   1681
 [672]     58     30   8027  17851  51909  10255    796    307   5994   1449     22
 [683]    263      6   1516   3173    215     36   1138   1712   3398  26685   6738
 [694]   2568      3   3967    887   2473    480    611    620    300   3409    666
 [705]   2968      2      1     95    207   7894    491    138   2957   1590     38
 [716]   6122      1      1   3461    878     13    705    167   2014  61381    228
 [727]   2941    467     53   1262   1054    461     15  27625     87    127   6476
 [738]     20    183      5      9    783     12  13014   4541     16   1813   3820
 [749]      4    838  35040   5807    630   1535     12      4    115   2705   2215
 [760]   5474   6074    413  24280     27   2039   4712     80   9712      7    872
 [771]     42   8123  41389     59     42     40     58     16   1217     76    501
 [782]    771   2002   2805   2923   1185     73    130   1854   1299    114    214
 [793]    674  23224   5373  18910     93   4042     36   1326   1226   5819   8565
 [804]      6     23   6757    363   2342   6000   8649   1486    251      2      1
 [815]     13    133      3    687    363   2050      7    230   6918      3      0
 [826]      2     26      2      2      1     17    783   3027   5010     20      6
 [837]     29     13    128      7    246   2595     18    751      1      0    850
 [848]     69   2554   2882      0      0      0   4508     35      1   3802   4418
 [859]   6728     47    498   3875    711     15   4362   2074   1724   2482   4247
 [870]   4964    311     18   1248   4877    206    160   3938      4      5      2
 [881]    752     34   1137   2592   2660    634  14466     19      1      1     10
 [892]   5272   1454     27   1644   2090      1   4392    843      1      7    629
 [903]   4351  10736    275     60     22      2   1199   1246    486      4      8
 [914]  38718      3      1   1576   7683     76      0  11118   1959     22      2
 [925]      6   2343      5    291    187   2998  24724      6     25      7   1212
 [936]      2     12    291     25      1    101     14   6835      7      3    607
 [947]   1816      1      1     11    105   7893   8324     24    923      3     99
 [958]     83      0    602   1349    750     82   3090   1334      9      1   8227
 [969]      2   2002   3823     11     11      1   5001   1142   4180    851   8345
 [980]      8    194      1     36      7    419   2152   1692     11      2     23
 [991]  29496   3526  15995    355   4054  37225      7   2697   2902     38
 [ reached getOption("max.print") -- omitted 25364 entries ]
plot(density(vector_of_ranges), log='x')
Warning in xy.coords(x, y, xlabel, ylabel, log) :
  1 x value <= 0 omitted from logarithmic plot

PCA

##### generate a PCA plot #######
dds <- DESeqDataSetFromMatrix(countData = matrix_of_data,colData = coldata,design = ~ dex)
dds <- dds[rowSums(counts(dds)) > 1,]

vst <- vst(dds,blind = FALSE)
plotPCA(vst, intgroup=c("dex"))

UMAP

matrix_of_data_t <- t(matrix_of_data)
df.umap = umap(matrix_of_data_t)

dimension1 <- range(df.umap$layout)
dimension2 <- dimension1
plot(dimension1, dimension2, type="n")
points(df.umap$layout[,1], df.umap$layout[,2], col=as.integer(coldata[,"dex"])+2, cex=2, pch=20)
labels.u = unique(coldata[,"dex"])
legend.text = as.character(labels.u)
legend.pos = "bottomleft"
legend.text = paste(as.character(labels.u), "")
 
legend(legend.pos, legend=legend.text, inset=0.03,col=as.integer(labels.u)+2,bty="n", pch=20, cex=1)

Summary for PCA/UMAP

#### Perform differential analysis on the samples from your two groups #####

deseq_object <- DESeq(dds)
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
-- replacing outliers and refitting for 308 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)
estimating dispersions
fitting model and testing
deseq_results <- results(deseq_object)
deseq_results <- lfcShrink(deseq_object,coef = 2, res = deseq_results )
using 'apeglm' for LFC shrinkage. If used in published research, please cite:
    Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
    sequence count data: removing the noise and preserving large differences.
    Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
head(deseq_results)
log2 fold change (MAP): dex SZ vs Control 
Wald test p-value: dex SZ vs Control 
DataFrame with 6 rows and 5 columns
           baseMean log2FoldChange     lfcSE    pvalue      padj
          <numeric>      <numeric> <numeric> <numeric> <numeric>
DDX11L1     15.2075      0.0185428  0.144337  0.785012  0.891035
WASH7P    1157.3822      0.1255759  0.110817  0.140908  0.348768
MIR6859-3   84.1270      0.1824412  0.131918  0.057109  0.200511
MIR6859-2   84.1270      0.1824412  0.131918  0.057109  0.200511
MIR6859-4   84.1270      0.1824412  0.131918  0.057109  0.200511
MIR6859-1   84.1270      0.1824412  0.131918  0.057109  0.200511
deseq_df <- deseq_results %>%
  # make into data.frame
  as.data.frame() %>%
  # the gene names are row names -- let's make them a column for easy display
  tibble::rownames_to_column("Gene") %>%
  # add a column for significance threshold results
  dplyr::mutate(threshold = padj < 0.05) %>%
  # sort by statistic -- the highest values will be genes with
  # higher expression in RPL10 mutated samples
  dplyr::arrange(dplyr::desc(log2FoldChange))
head(deseq_df)
plotCounts(dds, gene = "DDX11L1", intgroup = "dex")

Volcano plot

#volcano plot here
volcano_plot <- EnhancedVolcano::EnhancedVolcano(
  deseq_df,lab = deseq_df$Gene,x = "log2FoldChange",
  y = "padj",pCutoff = 0.01 )
Warning: Ignoring unknown parameters: xlim, ylim
volcano_plot

Summary of volcano plot

Heatmap

#heatmap
vst <- vst(dds,blind = FALSE)

sampleDists <- dist(t(assay(vst)))
sampleDists
             Sample_LI-01 Sample_LI-02 Sample_LI-03 Sample_LI-04 Sample_LI-05
Sample_LI-02    144.42095                                                    
Sample_LI-03     96.21431    133.97114                                       
Sample_LI-04     99.87450    139.71166     37.77497                          
Sample_LI-05     83.60813    156.00739    101.78338    103.29480             
Sample_LI-06    119.44816    116.22757    120.67800    123.34283     96.11964
Sample_LI-07     95.37821    155.36706    112.59969    113.70332     76.26127
Sample_LI-08     95.26113    141.14019    109.07132    108.09854     78.42244
Sample_LI-09     80.44241    130.44555     99.27533    104.59342     58.80801
Sample_LI-10     84.61049    159.95587    101.84089     97.85090     50.30949
Sample_LI-11     89.07397    124.77483     80.79587     83.05840     91.87756
Sample_LI-12     42.10256    153.45828     95.05450     93.44808     80.13373
Sample_LI-13     93.57608    146.53943     89.62124     92.59267     71.68491
Sample_LI-14     98.06298    144.25071     93.04808     91.18505     75.18324
Sample_LI-15     88.27419    145.39448     96.89075     99.89449     72.05981
Sample_LI-16     91.47799    134.87317     95.56142     94.36748     76.91570
Sample_LI-17    110.21659    156.31258    108.56673    112.58090     93.57011
Sample_LI-18    115.32256    165.33256    111.34182    106.54065     96.07806
Sample_LI-19    117.31859    167.04564    123.81656    126.89681    116.25921
Sample_LI-20    117.22968    160.07730    119.69261    118.73346    114.31221
Sample_LI-23    115.82222    159.04777    117.81344    119.68953    106.69378
Sample_LI-24    117.43344    161.46974    117.82703    114.48324    106.97566
Sample_LI-25    114.69350    134.34106     78.37575     79.54452    105.88925
Sample_LI-27    107.69827    150.74338     98.20624    102.74555    101.89783
             Sample_LI-06 Sample_LI-07 Sample_LI-08 Sample_LI-09 Sample_LI-10
Sample_LI-02                                                                 
Sample_LI-03                                                                 
Sample_LI-04                                                                 
Sample_LI-05                                                                 
Sample_LI-06                                                                 
Sample_LI-07    115.94728                                                    
Sample_LI-08    101.09101     39.36980                                       
Sample_LI-09     75.32779     84.06512     78.70855                          
Sample_LI-10    101.46811     77.90681     75.79231     61.25290             
Sample_LI-11    124.53992     90.01049     90.30398     96.56694     92.11518
Sample_LI-12    119.25132     91.88427     91.16393     84.22445     73.28430
Sample_LI-13    108.62476     78.17900     80.65945     81.27663     78.69382
Sample_LI-14    100.26206     80.47143     76.53043     83.31679     75.45347
Sample_LI-15    108.87130     68.31503     72.22288     79.79228     80.26742
Sample_LI-16     96.16823     75.87451     68.34821     79.38293     78.20575
Sample_LI-17    121.32108     79.21296     81.19500     97.19180     98.54813
Sample_LI-18    125.66399     82.89720     79.18897    107.02390     90.33651
Sample_LI-19    147.07707     92.33292     97.36230    118.90778    119.27992
Sample_LI-20    136.86663     91.68664     89.17978    115.67221    112.69894
Sample_LI-23    132.85037     86.20527     87.29910    108.25278    108.02152
Sample_LI-24    133.33938     86.94077     83.42641    112.45640    102.49730
Sample_LI-25    117.89541    115.89225    112.34720    106.37446    107.46017
Sample_LI-27    130.49242    101.04756    103.66152    101.12589    103.96036
             Sample_LI-11 Sample_LI-12 Sample_LI-13 Sample_LI-14 Sample_LI-15
Sample_LI-02                                                                 
Sample_LI-03                                                                 
Sample_LI-04                                                                 
Sample_LI-05                                                                 
Sample_LI-06                                                                 
Sample_LI-07                                                                 
Sample_LI-08                                                                 
Sample_LI-09                                                                 
Sample_LI-10                                                                 
Sample_LI-11                                                                 
Sample_LI-12     86.55308                                                    
Sample_LI-13     80.22686     89.71738                                       
Sample_LI-14     85.40616     90.37432     37.77026                          
Sample_LI-15     82.77390     86.44219     39.10570     46.18250             
Sample_LI-16     85.62742     86.45278     48.33529     39.87597     40.29908
Sample_LI-17    102.53927    108.27463     81.33364     84.02504     81.23823
Sample_LI-18    103.42611    104.93807     87.07794     80.62000     87.91855
Sample_LI-19    114.69949    117.15625    106.04336    110.89265    101.00510
Sample_LI-20    112.69567    113.06653    104.30384    102.93066     99.72611
Sample_LI-23    108.10184    113.69641     98.01200     99.75891     93.77137
Sample_LI-24    107.78078    110.80109    100.49780     96.56208     95.63683
Sample_LI-25     83.09330    111.47755     81.20942     83.53271     92.85541
Sample_LI-27     95.66504    108.89038     90.49695     96.61505     95.68051
             Sample_LI-16 Sample_LI-17 Sample_LI-18 Sample_LI-19 Sample_LI-20
Sample_LI-02                                                                 
Sample_LI-03                                                                 
Sample_LI-04                                                                 
Sample_LI-05                                                                 
Sample_LI-06                                                                 
Sample_LI-07                                                                 
Sample_LI-08                                                                 
Sample_LI-09                                                                 
Sample_LI-10                                                                 
Sample_LI-11                                                                 
Sample_LI-12                                                                 
Sample_LI-13                                                                 
Sample_LI-14                                                                 
Sample_LI-15                                                                 
Sample_LI-16                                                                 
Sample_LI-17     85.23606                                                    
Sample_LI-18     82.64446     52.15115                                       
Sample_LI-19    106.81797     76.73758     90.05378                          
Sample_LI-20     98.16905     76.69849     76.56972     43.88827             
Sample_LI-23     96.33758     61.38807     72.16698     58.01886     58.54225
Sample_LI-24     92.77982     71.02220     61.30604     70.22445     56.41068
Sample_LI-25     90.58481    116.67682    116.47200    136.97377    131.50994
Sample_LI-27    100.95401    104.69435    111.40654    113.49061    114.35801
             Sample_LI-23 Sample_LI-24 Sample_LI-25 Sample_LI-27 Sample_LI-28
Sample_LI-02                                                                 
Sample_LI-03                                                                 
Sample_LI-04                                                                 
Sample_LI-05                                                                 
Sample_LI-06                                                                 
Sample_LI-07                                                                 
Sample_LI-08                                                                 
Sample_LI-09                                                                 
Sample_LI-10                                                                 
Sample_LI-11                                                                 
Sample_LI-12                                                                 
Sample_LI-13                                                                 
Sample_LI-14                                                                 
Sample_LI-15                                                                 
Sample_LI-16                                                                 
Sample_LI-17                                                                 
Sample_LI-18                                                                 
Sample_LI-19                                                                 
Sample_LI-20                                                                 
Sample_LI-23                                                                 
Sample_LI-24     42.82230                                                    
Sample_LI-25    126.77162    125.40196                                       
Sample_LI-27    111.71844    114.67548    101.83543                          
             Sample_LI-29 Sample_LI-30 Sample_LI-31 Sample_LI-32 Sample_LI-33
Sample_LI-02                                                                 
Sample_LI-03                                                                 
Sample_LI-04                                                                 
Sample_LI-05                                                                 
Sample_LI-06                                                                 
Sample_LI-07                                                                 
Sample_LI-08                                                                 
Sample_LI-09                                                                 
Sample_LI-10                                                                 
Sample_LI-11                                                                 
Sample_LI-12                                                                 
Sample_LI-13                                                                 
Sample_LI-14                                                                 
Sample_LI-15                                                                 
Sample_LI-16                                                                 
Sample_LI-17                                                                 
Sample_LI-18                                                                 
Sample_LI-19                                                                 
Sample_LI-20                                                                 
Sample_LI-23                                                                 
Sample_LI-24                                                                 
Sample_LI-25                                                                 
Sample_LI-27                                                                 
             Sample_LI-34 Sample_LI-36 Sample_LI-37 Sample_LI-38 Sample_LI-39
Sample_LI-02                                                                 
Sample_LI-03                                                                 
Sample_LI-04                                                                 
Sample_LI-05                                                                 
Sample_LI-06                                                                 
Sample_LI-07                                                                 
Sample_LI-08                                                                 
Sample_LI-09                                                                 
Sample_LI-10                                                                 
Sample_LI-11                                                                 
Sample_LI-12                                                                 
Sample_LI-13                                                                 
Sample_LI-14                                                                 
Sample_LI-15                                                                 
Sample_LI-16                                                                 
Sample_LI-17                                                                 
Sample_LI-18                                                                 
Sample_LI-19                                                                 
Sample_LI-20                                                                 
Sample_LI-23                                                                 
Sample_LI-24                                                                 
Sample_LI-25                                                                 
Sample_LI-27                                                                 
             Sample_LI-40 Sample_LI-41 Sample_LI-42 Sample_LI-43 Sample_LI-44
Sample_LI-02                                                                 
Sample_LI-03                                                                 
Sample_LI-04                                                                 
Sample_LI-05                                                                 
Sample_LI-06                                                                 
Sample_LI-07                                                                 
Sample_LI-08                                                                 
Sample_LI-09                                                                 
Sample_LI-10                                                                 
Sample_LI-11                                                                 
Sample_LI-12                                                                 
Sample_LI-13                                                                 
Sample_LI-14                                                                 
Sample_LI-15                                                                 
Sample_LI-16                                                                 
Sample_LI-17                                                                 
Sample_LI-18                                                                 
Sample_LI-19                                                                 
Sample_LI-20                                                                 
Sample_LI-23                                                                 
Sample_LI-24                                                                 
Sample_LI-25                                                                 
Sample_LI-27                                                                 
             Sample_LI-45 Sample_LI-46 Sample_LI-47
Sample_LI-02                                       
Sample_LI-03                                       
Sample_LI-04                                       
Sample_LI-05                                       
Sample_LI-06                                       
Sample_LI-07                                       
Sample_LI-08                                       
Sample_LI-09                                       
Sample_LI-10                                       
Sample_LI-11                                       
Sample_LI-12                                       
Sample_LI-13                                       
Sample_LI-14                                       
Sample_LI-15                                       
Sample_LI-16                                       
Sample_LI-17                                       
Sample_LI-18                                       
Sample_LI-19                                       
Sample_LI-20                                       
Sample_LI-23                                       
Sample_LI-24                                       
Sample_LI-25                                       
Sample_LI-27                                       
 [ reached getOption("max.print") -- omitted 20 rows ]
sampleDistMatrix <- as.matrix( sampleDists )
rownames(sampleDistMatrix) <- paste( vst$dex, sep = " - " )
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)

pheatmap(sampleDistMatrix,clustering_distance_rows = sampleDists,
         clustering_distance_cols = sampleDists,col = colors)


topVarGenes <- head(order(rowVars(assay(vst)), decreasing = TRUE), 20)
topVarGenes
 [1] 23946 23945 24456 15776 24464  1660 10189 24311  1652 24481  6057 24463 22385
[14] 15849 24483 10106 10188 20848 10186 21054
mat  <- assay(vst)[topVarGenes, ]

mat<-mat-rowMeans(mat)
anno <- as.data.frame(colData(vst)[c("dex")])
pheatmap(mat, annotation_col = anno)

Method/Ontology 1

#Method 1 code
topVarGenesGO <- head(order(rowVars(assay(vst)), decreasing = TRUE), 100)
topVarGenesGO
  [1] 23946 23945 24456 15776 24464  1660 10189 24311  1652 24481  6057 24463 22385
 [14] 15849 24483 10106 10188 20848 10186 21054 17462 16399 24462    22 24480 15850
 [27] 24129  2580 20649 24457 20650  9553 24465 12523  4972  4259 10185 22806 10187
 [40] 10184 15491  3454 20648  9739 21229 18715 17622 21773  3455 22294  7321 10181
 [53] 10908 20645 21227 23012 20653  5159  4477  5001  3052 15389 23982 13827 21651
 [66]  3453 20454  3720  4449 17605 19980 11498 17055  1707 17248  6386  6061  1509
 [79] 20557 23515 23131  3711  3755 19040 22399  6996  5606  2536 18929 11051  6170
 [92]  1511 10518 18091 15206  2875 20651 14865  7057  2894
mat_100  <- assay(vst)[topVarGenesGO, ]

gostres <- gost(query = rownames(mat_100), 
                organism = "hsapiens", ordered_query = FALSE, 
                multi_query = FALSE, significant = TRUE, exclude_iea = FALSE, 
                measure_underrepresentation = FALSE, evcodes = FALSE, 
                user_threshold = 0.05, correction_method = "g_SCS", 
                domain_scope = "annotated", custom_bg = NULL, 
                numeric_ns = "", sources = NULL, as_short_link = FALSE)

names(gostres)
[1] "result" "meta"  
head(gostres$result, 6)
names(gostres$meta)
[1] "query_metadata"  "result_metadata" "genes_metadata"  "timestamp"      
[5] "version"        
gostplot(gostres, capped = TRUE, interactive = TRUE)
plot <- gostplot(gostres, capped = FALSE, interactive = FALSE)
plot

publish_gosttable(gostres, highlight_terms = gostres$result[c(1:2,10,120),],
                  use_colors = TRUE, 
                  show_columns = c("source", "term_name", "term_size", "intersection_size"),
                  filename = NULL)
The input 'highlight_terms' is a data.frame. The column 'term_id' will be used.

summary of Method/Ontology 1

Method/Ontology 2

#Method 2 code
#get differentially expressed genes
dds <- DESeqDataSetFromMatrix(countData = matrix_of_data,colData = coldata,design = ~ dex)
res <- results(DESeq(dds))
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
-- replacing outliers and refitting for 308 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)
estimating dispersions
fitting model and testing
#store values
geneList = res[,2]
#create list of genes
names_list = rownames(res)

#lead gene names into symbols variable
symbols <- rownames(res)
#map symbols to IDs
gene_ids <- mapIds(org.Hs.eg.db, symbols, 'ENTREZID', 'SYMBOL')
'select()' returned 1:many mapping between keys and columns
#update ID's in list of gene names
for(i in 1:26364){
  names_list[i] = gene_ids[names_list[i]]
}
names(geneList) = names_list
geneList = sort(geneList, decreasing = TRUE)
gene <- names(geneList)[abs(geneList) > 1.5]
x <- enrichDO(gene          = gene,
              ont           = "DO",
              pvalueCutoff  = 0.05,
              pAdjustMethod = "BH",
              universe      = names(geneList),
              minGSSize     = 5,
              maxGSSize     = 500,
              qvalueCutoff  = 0.05,
              readable      = TRUE)
output_table <- x@result
setDT(output_table)
output_table
y <- gseDO(geneList,
           minGSSize     = 120,
           pvalueCutoff  = 0.2,
           pAdjustMethod = "BH",
           verbose       = FALSE)
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam,  :
  There are ties in the preranked stats (6.27% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam,  :
  There are duplicate gene names, fgsea may produce unexpected results.
Warning in serialize(data, node$con) :
  'package:stats' may not be available when loading
Warning in serialize(data, node$con) :
  'package:stats' may not be available when loading
Warning in serialize(data, node$con) :
  'package:stats' may not be available when loading
Warning in serialize(data, node$con) :
  'package:stats' may not be available when loading
Warning in serialize(data, node$con) :
  'package:stats' may not be available when loading
Warning in serialize(data, node$con) :
  'package:stats' may not be available when loading
Warning in serialize(data, node$con) :
  'package:stats' may not be available when loading
Warning in serialize(data, node$con) :
  'package:stats' may not be available when loading
Warning in serialize(data, node$con) :
  'package:stats' may not be available when loading
Warning in serialize(data, node$con) :
  'package:stats' may not be available when loading
output_table <- y@result
setDT(output_table)
output_table

summary of Method/Ontology 2

Method/Ontology 3

summary of Method/Ontology 3

---
title: "R Notebook"
output: html_notebook
---

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE119290

```{r}
if (!requireNamespace("knitr", quietly = TRUE))
    install.packages("knitr")
if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
if (!("DESeq2" %in% installed.packages())) {
  BiocManager::install("DESeq2", update = FALSE)
}
if (!("EnhancedVolcano" %in% installed.packages())) {
  BiocManager::install("EnhancedVolcano", update = FALSE)
}
if (!("apeglm" %in% installed.packages())) {
  BiocManager::install("apeglm", update = FALSE)
}
if (!("pheatmap" %in% installed.packages())) {
  BiocManager::install("pheatmap", update = FALSE)
}
if (!("gprofiler2" %in% installed.packages())) {
  BiocManager::install("gprofiler2", update = FALSE)
}
if (!("clusterProfiler" %in% installed.packages())) {
  BiocManager::install("clusterProfiler", update = FALSE)
}
if (!("M3C" %in% installed.packages())) {
  BiocManager::install("M3C", update = FALSE)
}
```


```{r}
library(M3C)
library(umap)
library(Rtsne)
library(clusterProfiler)
library(gprofiler2)
library ("pheatmap")
library ("RColorBrewer")
library(magrittr)
library(matrixStats)
library(ggplot2)
library(DESeq2)
library(DOSE)
library('org.Hs.eg.db')
library(data.table)
```



```{r}
###### Load the data into R.######
matrix_of_data <- as.matrix(read.table("GSE119290_Readhead_2018_RNAseq_gene_counts.txt"))
coldata<-read.csv("coldata.csv",header = T,row.names=1,stringsAsFactors=T)
```



```{r}
##### calculate per-gene expression ranges and generating a density plot #####
matrix_of_ranges <- rowRanges(matrix_of_data, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(matrix_of_data), useNames = NA)

vector_of_ranges <- 0
for(i in 1:26364) {
  vector_of_ranges[i] <- matrix_of_ranges[i,2]-matrix_of_ranges[i,1]
}
vector_of_ranges
plot(density(vector_of_ranges), log='x')
```

PCA


```{r}
##### generate a PCA plot #######
dds <- DESeqDataSetFromMatrix(countData = matrix_of_data,colData = coldata,design = ~ dex)
dds <- dds[rowSums(counts(dds)) > 1,]

vst <- vst(dds,blind = FALSE)
plotPCA(vst, intgroup=c("dex"))
```

UMAP

```{r}
matrix_of_data_t <- t(matrix_of_data)
df.umap = umap(matrix_of_data_t)

dimension1 <- range(df.umap$layout)
dimension2 <- dimension1
plot(dimension1, dimension2, type="n")
points(df.umap$layout[,1], df.umap$layout[,2], col=as.integer(coldata[,"dex"])+2, cex=2, pch=20)
labels.u = unique(coldata[,"dex"])
legend.text = as.character(labels.u)
legend.pos = "bottomleft"
legend.text = paste(as.character(labels.u), "")
 
legend(legend.pos, legend=legend.text, inset=0.03,col=as.integer(labels.u)+2,bty="n", pch=20, cex=1)
```

Summary for PCA/UMAP


```{r}
#### Perform differential analysis on the samples from your two groups #####

deseq_object <- DESeq(dds)
deseq_results <- results(deseq_object)
deseq_results <- lfcShrink(deseq_object,coef = 2, res = deseq_results )
head(deseq_results)

deseq_df <- deseq_results %>%
  # make into data.frame
  as.data.frame() %>%
  # the gene names are row names -- let's make them a column for easy display
  tibble::rownames_to_column("Gene") %>%
  # add a column for significance threshold results
  dplyr::mutate(threshold = padj < 0.05) %>%
  # sort by statistic -- the highest values will be genes with
  # higher expression in RPL10 mutated samples
  dplyr::arrange(dplyr::desc(log2FoldChange))
head(deseq_df)
plotCounts(dds, gene = "DDX11L1", intgroup = "dex")
```


Volcano plot

```{r}
#volcano plot here
volcano_plot <- EnhancedVolcano::EnhancedVolcano(
  deseq_df,lab = deseq_df$Gene,x = "log2FoldChange",
  y = "padj",pCutoff = 0.01 )

volcano_plot
```

Summary of volcano plot


Heatmap

```{r}
#heatmap
vst <- vst(dds,blind = FALSE)

sampleDists <- dist(t(assay(vst)))
sampleDists


sampleDistMatrix <- as.matrix( sampleDists )
rownames(sampleDistMatrix) <- paste( vst$dex, sep = " - " )
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)

pheatmap(sampleDistMatrix,clustering_distance_rows = sampleDists,
         clustering_distance_cols = sampleDists,col = colors)

topVarGenes <- head(order(rowVars(assay(vst)), decreasing = TRUE), 20)
topVarGenes
mat  <- assay(vst)[topVarGenes, ]

mat<-mat-rowMeans(mat)
anno <- as.data.frame(colData(vst)[c("dex")])
pheatmap(mat, annotation_col = anno)
```

Method/Ontology 1
```{r}
#Method 1 code
topVarGenesGO <- head(order(rowVars(assay(vst)), decreasing = TRUE), 100)
topVarGenesGO
mat_100  <- assay(vst)[topVarGenesGO, ]

gostres <- gost(query = rownames(mat_100), 
                organism = "hsapiens", ordered_query = FALSE, 
                multi_query = FALSE, significant = TRUE, exclude_iea = FALSE, 
                measure_underrepresentation = FALSE, evcodes = FALSE, 
                user_threshold = 0.05, correction_method = "g_SCS", 
                domain_scope = "annotated", custom_bg = NULL, 
                numeric_ns = "", sources = NULL, as_short_link = FALSE)

names(gostres)
head(gostres$result, 6)
names(gostres$meta)
gostplot(gostres, capped = TRUE, interactive = TRUE)
plot <- gostplot(gostres, capped = FALSE, interactive = FALSE)
plot
publish_gosttable(gostres, highlight_terms = gostres$result[c(1:2,10,120),],
                  use_colors = TRUE, 
                  show_columns = c("source", "term_name", "term_size", "intersection_size"),
                  filename = NULL)


```

summary of Method/Ontology 1

Method/Ontology 2
```{r}
#Method 2 code
#get differentially expressed genes
dds <- DESeqDataSetFromMatrix(countData = matrix_of_data,colData = coldata,design = ~ dex)
res <- results(DESeq(dds))
#store values
geneList = res[,2]
#create list of genes
names_list = rownames(res)

#lead gene names into symbols variable
symbols <- rownames(res)
#map symbols to IDs
gene_ids <- mapIds(org.Hs.eg.db, symbols, 'ENTREZID', 'SYMBOL')

#update ID's in list of gene names
for(i in 1:26364){
  names_list[i] = gene_ids[names_list[i]]
}
names(geneList) = names_list
geneList = sort(geneList, decreasing = TRUE)
gene <- names(geneList)[abs(geneList) > 1.5]
x <- enrichDO(gene          = gene,
              ont           = "DO",
              pvalueCutoff  = 0.05,
              pAdjustMethod = "BH",
              universe      = names(geneList),
              minGSSize     = 5,
              maxGSSize     = 500,
              qvalueCutoff  = 0.05,
              readable      = TRUE)
output_table <- x@result
setDT(output_table)
output_table
```
```{r}
y <- gseDO(geneList,
           minGSSize     = 120,
           pvalueCutoff  = 0.2,
           pAdjustMethod = "BH",
           verbose       = FALSE)
output_table <- y@result
setDT(output_table)
output_table
```

summary of Method/Ontology 2

Method/Ontology 3
```{r}
ego3 <- gseGO(geneList     = geneList,
              OrgDb        = org.Hs.eg.db,
              ont          = "CC",
              minGSSize    = 100,
              maxGSSize    = 500,
              pvalueCutoff = 0.05,
              verbose      = FALSE)
output_table <- ego3@result
setDT(output_table)
output_table
```

summary of Method/Ontology 3